Artificial intelligence microgrid and distributed energy resources planning platform

ABSTRACT

The embodiments disclosed in this document are directed to an AI-enabled microgrid and DER planning platform that uses AI methods and takes into account cost calculations, emission calculations, technology investments and operation. In an embodiment, the computing platform is deployed on a network (cloud computing platform) that can be accessed by a variety of stakeholders (e.g., investors, technology vendors, energy providers, regulatory authorities). In an embodiment, the planning platform implements machine learning (e.g., neural networks) to estimate various planning parameters, where the neural networks are trained on observed data from real-world microgrid/minigrid and DER projects.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority from Provisional PatentApplication No. 62/582,064, filed Nov. 6, 2017, for “ArtificialIntelligence (AI) Microgrid and Distributed Energy Resources (DER)Planning Platform,” the entire contents of which is incorporated hereinby reference.

TECHNICAL FIELD

The subject matter of this application relates generally to cloudcomputing and computer information systems applications for energygeneration and usage.

BACKGROUND

Microgrid and Distributed Energy Resources (DER) project developmentcosts are high due to missing technical and geospatial information andlimited computer power to analyze larger geographic regions of interestfor investors, technology vendors, energy providers and regulatoryauthorities. There is no holistic online solution for analyzingbuildings or geographic regions in a simple and fast manner. Variousdata is needed to assess the costs and environmental potential forcertain buildings, building types, industries or geographic regions.This data includes but is not limited to: energy and energy demand data,tariff data, technology data, weather data (e.g., solar radiation ortemperature) and any other suitable data. A conventional approach is tomodel each building individually and collect all the data that isneeded. Typically, such a process can take weeks. Thus, such an approachis not viable for hundreds or thousands of projects, buildings or largerareas containing many different customers.

Microgrid and DER solutions often include a wide variety oftechnologies, including but not limited to: photovoltaics, electricstorage, heat storage, cold storage, solar thermal, heat pumps (groundand air), electric vehicles, hydrogen and hydrogen storage, flowbatteries, absorption and absorption chillers, centralized cooling andheating, internal combustion engines, gas turbines, micro-turbines, allkinds of fuel cells, combined heat and power, cooling technologies andany other suitable technologies.

Microgrid and DER solutions have multiple energy carriers as input,including but not limited to: natural gas, hydrogen, biogas, methane,diesel, biomass, sun, wind, hydro and any other suitable energycarriers.

Microgrid and DER solutions are efficiency measures (e.g., buildingupgrades, window changes) to reduce the overall energy demand forheating, cooling, electricity (e.g., lighting or computing, and anyother suitable efficiency measures.

Microgrids and DER solutions are demand response measures to shift,reduce or cut power demand (e.g., electricity or heating).

To evaluate the attractiveness of a planning project in terms ofannualized investment costs, net present value, internal rate of return,capital expenditure (CAPEX), operating expense (OPEX), efficiency,environmental impact as CO₂ emissions, NOx emissions or CO emissions,sophisticated mathematical models are needed. Such models can usesimulation or optimization techniques. Such models need to combine thecharacteristics of the technologies (e.g., specific investment costs as$/kW or $/kWh, electrical and thermal efficiency, charging anddischarging efficiencies, minimum state of the charge, maintenancecosts) with load profiles (e.g., on an hourly or 1-15 min basis) fordifferent end uses (e.g., electric, heating, domestic hot water,cooling, natural gas, process heat in the industry), energy prices,tariffs and environmental constraints (e.g. available space forphotovoltaics). Based on available information and other assumptions anddata (e.g., solar radiation or wind data), the simulation and/oroptimization techniques calculate an optimal technology mix, costs,CAPEX, OPEX, and an environmental impact for the sites, places,buildings, or geographic regions under consideration.

The conventional data collection and modelling approach described aboveis burdensome and prohibits modeling parts of cities, counties, states,or whole countries. Moreover, investors, technology vendors, energyproviders, regulatory authorities often look for methods to simulateand/or optimize large numbers of customers, buildings, areas, countiesto receive guidance on their possible revenues, technology potentials,impact on service area, or regulatory issues.

Conventional solutions use “typical” building models, examples, or usecases in each geographic region with detailed input data to build adatabase for those buildings based on simulation and/or optimizationtechniques, and then use the database for other analyses. This approach,however, limits the results of the analyses to the specific parametersselected and there is no efficient way to scale the results to othercombinations of input data, regions, change in energy consumption,tariffs and other assumptions which can change with each analysis.

SUMMARY

The embodiments disclosed herein are directed to an AI-enabled microgridand DER planning platform that uses AI methods and takes into accountcost calculations, emission calculations, technology investments andoperation. In an embodiment, the computing platform is deployed on anetwork (cloud computing platform) that can be accessed by a variety ofstakeholders (e.g., investors, technology vendors, energy providers,regulatory authorities). In an embodiment, the planning platformimplements machine learning (e.g., neural networks) to estimate variousplanning parameters, where the neural networks are trained on observeddata from real-world microgrid/minigrid and DER projects.

In an embodiment, a method comprises: receiving, by one or moreprocessors, a first set of input planning parameters for a microgrid ordistributed energy resources (DER) system; receiving, by the one or moreprocessors, input data describing one or more facilities of themicrogrid or DER system; generating, by the one or more processors,training data based on a formal method that uses a mathematical model;training, by the one or more processors, an artificial intelligence (AI)planning model using the training data; applying, by the one or moreprocessors, the AI planning model to a second set of input planningparameters; and reporting, by the one or more processors, a planningsolution for the second set of input planning parameters. The first orsecond set of input planning parameters can include at least one ofhourly load profiles for each end use, energy provider data, natural gascosts, solar radiation data, hourly average outside temperatures ortechnology options.

In another embodiment, the AI planning model includes machine learningand the method further comprises: generating, by the one or moreprocessors, indices for the input data; training, by the one or moreprocessors, a machine learning model using the training data; andapplying, by the one or more processors, the machine learning model to asecond set of input planning parameters. The machine learning model canbe a multilayer neural network. The indices can include but are notlimited to: energy intensity for a building type as an indicator of anhourly load; geographic location as an indicator for solar radiation orhourly average outside temperature for a utility service area; customertype as an indicator for an energy provider or utility costs; andgeographic location as an indicator for changed technology costs.

Other embodiments are directed to systems, apparatuses andnon-transitory computer-readable mediums. For example, if enoughimplemented microgrid/minigrid and DER installations become available,then real observed project data can be used for the AI training insteadof mathematical calculations.

Particular implementations disclosed herein provide one or more of thefollowing advantages. The disclosed computing platform implements anAI-based planning tool for microgrids and DER systems that estimates orpredicts possible revenues, technology potentials, electrical impact ona service area, and regulatory issues for various stakeholders,including but not limited to: investors, technology vendors, energyproviders, regulatory authorities, large numbers of customers, buildingareas, industries and counties or countries.

The planning tool uses simulation and/or optimization techniques (e.g.,mixed integer, linear, non-linear, stochastic approaches) for planning.In an embodiment, real observed project data sets are used to createtraining sets of buildings and areas that are used to train neuralnetworks (e.g., train a multilayer convolutional neural network) torecognize various planning parameters, including but not limited to:annualized investment costs, net present value, internal rate of return,CAPEX, OPEX, efficiency, environmental impact as CO₂ emissions, NOxemissions or CO emissions for buildings, building types, campuses,microgrids/minigrids, and areas, including power flow issues based onapproximated or real observed data.

The disclosed embodiments include a cloud based information Technology(IT) computing platform that addresses microgrid and DER planningprojects involving many different buildings, building types, and largergeographic areas. The disclosed embodiments save time, costs, andcomputer power and allow fast analyses without collecting enormousamounts of data or incurring long computing times after the system hasbeen trained. An advantage of the microgrid and DER planning tool is theuse of self-learning databases and indices instead of detailed inputdata to minimize the data collection process for the user.

Investors can use the platform to assess the attractiveness of a projectwithin minutes or seconds without the need of burdensome and timeconsuming data collection processes. Investors will be able to identify,for example, regions, areas, and building types, industries, as well ascustomers with a certain project rate of return, net present value,CAPEX, OPEX and this will allow them to target the most effective andattractive customers without a long pre-selection process.

Technology vendors can use the platform to assess the attractiveness oftheir technologies in combination with other technologies. This willallow them to better estimate their sales, revenues, and technologypenetration and this will increase their financial planningcapabilities. Technology vendors can assess the environmental impact(e.g., CO₂, NOx, CO Emissions among possibly others) for theirtechnologies quickly, separated by the different customer segments(e.g., building types).

Energy providers (and/or utilities) can use the platform to assess theimpact on their energy sales and electric grid/network very quickly.Energy providers (and/or utilities) can assess the impact on power flowand grid stability, influenced by the different DERs and microgridprojects, very quickly, without the need of supercomputing.

Regulatory authorities can use the platform to assess the benefits(e.g., network upgrade costs versus local energy markets) for society byenabling certain microgrid and DER technologies at certain customers,buildings, industries, and regions. Regulatory authorities can improvethe capabilities to regulate energy providers/utilities by a bettermodelling and understanding of their costs inflicted by microgrid andDER projects. On the other hand, this also means that microgrid and DERprojects can be placed strategically in a utility or energy providernetwork to avoid higher energy costs for the society.

The details of the disclosed implementations are set forth in theaccompanying drawings and the description below. Other features, objectsand advantages are apparent from the description, drawings and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is conceptual block diagram illustrating an AI-enabled microgridand DER planning platform, according to an embodiment.

FIG. 2 is a flow diagram of a deep learning process performed by a deeplearning module of the AI-enabled microgrid and DER planning platform ofFIG. 1 , according to an embodiment.

FIG. 3 is an example parameter matrix (PM_(i)), according to anembodiment.

FIG. 4 is an example result matrix (RM_(i)), according to an embodiment

FIG. 5 is an example mobile device architecture for implementing thefeatures and processes described in reference to FIGS. 1-4 .

FIG. 6 is an example server computer architecture for implementing thefeatures and processes described in reference to FIGS. 1-4 .

DETAILED DESCRIPTION

FIG. 1 is conceptual block diagram illustrating an AI-enabled microgrid(also referred to as “minigrid”) and Distributed Energy Resources (DER)planning platform 100, according to an embodiment. In an embodiment, theAI-enabled microgrid and DER planning platform 100 includes two modules:deep learning module 101 and use module 102. Modules 101, 102collectively comprise the platform 100 and are configured to estimatemicrogrid and DER planning parameters for a particular site, place,building, or geographic region under consideration by a stakeholder(hereinafter collectively referred to as a “facility”). The planningparameters include but are not limited to: technology mix, costs, CAPEX,OPEX and environmental impact. Platform 100 can be implemented using acloud-based computing platform, where stakeholders access the platformthrough a network (e.g., the Internet) using a desktop computer ormobile device. The AI used by platform 100 can include one or moreformal methods (e.g., one or more mathematical models) or one or moremachine learning algorithms (e.g., one or more multilayer convolutionalneural networks).

As used herein, a “power grid” is a network of power providers andconsumers that are connected by transmission and distribution lines andoperated by one or more control centers. As used herein a “microgrid” or“minigrid” is a group of interconnected loads and distributed energyresource (DER) systems within defined electrical boundaries that act asa single controllable entity with respect to the power grid. Amicrogrid/minigrid can connect and disconnect from the power grid toenable it to operate in both grid-connected or island-mode. As usedherein, “DER” systems are small-scale power generation or storagetechnologies (typically in the range of 1 kW to 10,000 kW) used toprovide an alternative to, or an enhancement of, a traditional powergrid. A microgrid/minigrid can also include heat, cooling and otherforms of energy delivery.

Example Processes

FIG. 2 is a flow diagram of a deep learning process performed by deeplearning module 102 of the microgrid and DER planning platform 100 ofFIG. 1 , according to an embodiment.

Deep Learning Mode

In an embodiment, the deep learning module 101 implements process 200using the computer architecture shown in FIG. 6 . Process 200 begins byselecting input planning parameters (201), collecting input data foreach facility of a microgrid/DER (202) and generating indices for use astraining data for an AI model (203). The indices are used to simplifydetailed input data for facilities, which can be easily collected by theuser of the platform 100 using the use module 102. The result of theforegoing steps 201-203 is a Parameter Matrix (PM₁) for each facility(e.g., for each building, building type, industry), where the subscript“i” is the ith facility under consideration by a stakeholder (e.g., aninvestor, technology vendor, energy provider, regulatory authority).

The PM₁ contains indices describing the facilities and/or energy demandsof the microgrid/DER, such as building type (e.g., new building or oldbuilding),” facility location (e.g., “Los Angeles” or “London”) andenergy demand (e.g., solar radiation). An example of PM_(i) data isshown in FIG. 3 for a warehouse located in San Francisco that was builtbefore 1980. The example PM₁ data includes tariff data, technologyvariable costs and a monthly breakdown of total electricity costs, totalcooling costs, total combined electricity and cooling costs, peakelectricity usage per week and per weekend.

Process 200 continues by calculating a result matrix RM_(i) using an AIplanning model (204). The AI planning model combines the characteristicsof technologies (e.g., specific investment costs as $/kW or $/kWh,electrical and thermal efficiency, charging and dischargingefficiencies, minimum state of the charge, maintenance costs) with loadprofiles (e.g., on an hourly or 1-15 min basis) for different end uses(e.g., electric, heating, domestic hot water, cooling, natural gas,process heat in the industry), energy prices, tariffs and environmentalconstraints (e.g. available space for photovoltaics).

In an alternative embodiment, the model is based on real planningproject data rather than a model. The result of step (204) is the resultMatrix (RM_(i)) for each building, building type, industry, etc. Anexample RM₁ is shown in FIG. 4 for the warehouse located in SanFrancisco that was built before 1980. The RM_(i) data can include but isnot limited to: annual energy costs, annual CO2 emissions, totalelectric costs, total electricity costs and total annual electricitypurchase. Also shown in FIG. 4 is a technology mix example (e.g.,photovoltaic, stationary battery) and breakdown of total monthlyelectricity costs and electricity purchases for the warehouse.

Process 200 continues by training the machine learning model (e.g., amultilayer convolutional neural network, forward neural network) (205)using the RM_(i) and PM_(i) as a training set. The result of thetraining is a Training Results Matrix TRM_(i). In an embodiment, process200 preforms error vector checking (206) to evaluate the performance ofthe machine learning algorithm. For example, an error vector can begenerated by comparing the RM_(i) with the TRM_(i) (e.g., computing thedifference). If the machine learning model performance does not satisfya specified performance criteria, then an additional training set isselected and the training repeats. If the machine learning modelperformance is satisfactory, the resulting AI-M is made available to theuse module (207). In an embodiment, whether or not the AI performance issatisfactory is evaluated based on a comparison of the error vector witha threshold value.

In an embodiment, the trained AI-M has the same data format as thePM_(i), including simplified data points needed to process input data inthe use module 102, and a mathematical description of the trainedmachine learning model, including but not limited to: weights for thedifferent nodes in the machine learning model, the number of layers inthe machine learning model, and the link structure of the nodes. Ifmachine learning methods (e.g., neural networks) are used, the modeldescription needs to be complete so that it can be built and replicatedin the use module 102. The data format and simplified data (indices)need to follow the specifications from step 203 of the deep learningprocess 200. The reduction of planning data complexity to simple indicesin PM_(i) is advantageous in that the indices reduce computing timesconsiderably and increase usability of the AI-enabled planning platform100.

In an embodiment, a training set for the deep learning process 200includes the calculated indices PM₁, and the detailed results RM_(i)from the simulation or optimization for each training building, buildingtype, industry, or geographic region. Instead of using simulated data,real data from a completed microgrid/DER planning project can be used asthe RM_(i) in the deep learning module 101. In this circumstance, step204 is not needed and the RM_(i) includes the real planning projectdata.

Use Module

The use module 102 performs the following: (a) collects indices for theindividual building, customer, industry, or area of interest based onthe same data format as the PM_(i), an example of which is shown in FIG.3 ; (b) uses the collected indices to run the trained AI-M on theplanning platform 100; and (c) calculates output for planningparameters, including but not limited to: annualized investment costs,net present value, internal rate of return, CAPEX, OPEX, efficiency,environmental impact as CO₂ emissions, NOx emissions or CO emissions forthe considered building, building type, campus, Microgrid, and/or areaof interest. The use module 102 can be implemented as a web-based toolor run locally on any computer, handheld device, mobile phone, ortablet. An example mobile device architecture for implementing the usemodule 102 is shown in FIG. 5 . In an embodiment, the use module 102uses one or more databases to reduce the user's labor and time byproviding locational input data or tariff data and any other appropriatedata. The use module 102 also produces reports for the user.

Example Client Architecture

FIG. 5 is a block diagram of example mobile device architecture 500 forimplementing the features and processes described in reference to FIGS.1-4 . Architecture 500 may be implemented in any mobile device forimplementing the features and processes described in reference to FIGS.1-4 , including but not limited to portable computers, smart phones andtablet computers, game consoles, wearable computers and the like.Architecture 500 may include memory interface 502, data processor(s),image processor(s) or central processing unit(s) 504, and peripheralsinterface 506. Memory interface 502, processor(s) 504 or peripheralsinterface 506 may be separate components or may be integrated in one ormore integrated circuits. One or more communication buses or signallines may couple the various components.

Sensors, devices, and subsystems may be coupled to peripherals interface506 to facilitate multiple functionalities. For example, motion sensor510, light sensor 512, and proximity sensor 514 may be coupled toperipherals interface 506 to facilitate orientation, lighting, andproximity functions of the device. For example, in some implementations,light sensor 512 may be utilized to facilitate adjusting the brightnessof touch surface 546. In some implementations, motion sensor 510 (e.g.,an accelerometer, gyros) may be utilized to detect movement andorientation of the device. Accordingly, display objects or media may bepresented according to a detected orientation (e.g., portrait orlandscape). Other sensors may also be connected to peripherals interface506, such as a temperature sensor, a biometric sensor, or other sensingdevice, to facilitate related functionalities.

Location processor 515 (e.g., GPS receiver chip) may be connected toperipherals interface 506 to provide geo-referencing. Electronicmagnetometer 516 (e.g., an integrated circuit chip) may also beconnected to peripherals interface 506 to provide data that may be usedto determine the direction of magnetic North. Thus, electronicmagnetometer 516 may be used with an electronic compass application.

Camera subsystem 520 and an optical sensor 522, e.g., a charged coupleddevice (CCD) or a complementary metal-oxide semiconductor (CMOS) opticalsensor, may be utilized to facilitate camera functions, such asrecording photographs and video clips.

Communication functions may be facilitated through one or morecommunication subsystems 524. Communication subsystem(s) 524 may includeone or more wireless communication subsystems. Wireless communicationsubsystems 524 may include radio frequency receivers and transmittersand/or optical (e.g., infrared) receivers and transmitters. Wiredcommunication systems 524 may include a port, e.g., a Universal SerialBus (USB) port or some other wired port connection that may be used toestablish a wired connection to other computing devices, such as othercommunication devices, network access devices, a personal computer, aprinter, a display screen, or other processing devices capable ofreceiving or transmitting data.

The specific design and implementation of the communication subsystem524 may depend on the communication network(s) or medium(s) over whichthe device is intended to operate. For example, a device may includewireless communication subsystems designed to operate using known orstandardized protocols, including but not limited to: global system formobile communications (GSM), GPRS, enhanced data GSM environment (EDGE),IEEE 802.x (e.g., Wi-Fi, Wi-Max), code division multiple access (CDMA),Near Field Communications (NFC), Bluetooth® (including classicBluetooth® and Bluetooth® low energy (BLE)). Wireless communicationsubsystems 524 may include hosting protocols such that the device may beconfigured as a base station for other wireless devices. As anotherexample, the communication subsystems may allow the device tosynchronize with a host device using one or more protocols, such as, forexample, the TCP/IP protocol, HTTP protocol, UDP protocol, and any otherknown or standardized protocol.

Audio subsystem 526 may be coupled to a speaker 528 and one or moremicrophones 530 to facilitate voice-enabled functions, such as voicerecognition, voice replication, digital recording, and telephonyfunctions.

I/O subsystem 540 may include touch controller 542 and/or other inputcontroller(s) 544. Touch controller 542 may be coupled to a touchsurface 546. Touch surface 546 and touch controller 542 may, forexample, detect contact and movement or break thereof using any of anumber of touch sensitivity technologies, including but not limited tocapacitive, resistive, infrared, and surface acoustic wave technologies,as well as other proximity sensor arrays or other elements fordetermining one or more points of contact with touch surface 546. In oneimplementation, touch surface 546 may display virtual or soft buttonsand a virtual keyboard, which may be used as an input/output device bythe user.

Other input controller(s) 544 may be coupled to other input/controldevices 548, such as one or more buttons, rocker switches, thumb-wheel,infrared port, USB port, and/or a pointer device such as a stylus. Theone or more buttons (not shown) may include an up/down button for volumecontrol of speaker 528 and/or microphone 530.

In some implementations, architecture 500 may present recorded audioand/or video files, such as MP3, AAC, and MPEG video files. In someimplementations, architecture 500 may include the functionality of anMP3 player and may include a pin connector for tethering to otherdevices. Other input/output and control devices may be used.

Memory interface 502 may be coupled to memory 550. Memory 550 mayinclude high-speed random access memory or non-volatile memory, such asone or more magnetic disk storage devices, one or more optical storagedevices, or flash memory (e.g., NAND, NOR). Memory 550 may storeoperating system 552, such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWSor an embedded operating system such as VxWorks. Operating system 552may include instructions for handling basic system services and forperforming hardware dependent tasks. In some implementations, operatingsystem 552 may include a kernel (e.g., UNIX kernel).

Memory 550 may also store communication instructions 554 to facilitatecommunicating with one or more additional devices, one or more computersor servers, including peer-to-peer communications, as described inreference to FIGS. 1-4 . Communication instructions 554 may also be usedto select an operational mode or communication medium for use by thedevice, based on a geographic location (obtained by the GPS/Navigationinstructions 568) of the device. Memory 550 may include graphical userinterface instructions 556 to facilitate graphical user interfaceprocessing, including a touch model for interpreting touch inputs andgestures; sensor processing instructions 558 to facilitatesensor-related processing and functions; phone instructions 560 tofacilitate phone-related processes and functions; electronic messaginginstructions 562 to facilitate electronic-messaging related processesand functions; web browsing instructions 564 to facilitate webbrowsing-related processes and functions; media processing instructions566 to facilitate media processing-related processes and functions;GPS/Navigation instructions 568 to facilitate GPS and navigation-relatedprocesses; camera instructions 570 to facilitate camera-relatedprocesses and functions; and other instructions 572 for performing someor all of the processes, as described in reference to FIGS. 1-4 .

Each of the above identified instructions and applications maycorrespond to a set of instructions for performing one or more functionsdescribed above. These instructions need not be implemented as separatesoftware programs, procedures, or modules. Memory 550 may includeadditional instructions or fewer instructions. Furthermore, variousfunctions of the device may be implemented in hardware and/or insoftware, including in one or more signal processing and/or applicationspecific integrated circuits (ASICs).

Example Server Architecture

FIG. 6 is a block diagram of example server computer system architecture600 for implementing the features and processes described in referenceto FIGS. 1-4 . Other architectures are possible, including architectureswith more or fewer components. In some implementations, architecture 600includes one or more processors 602 (e.g., dual-core Intel® Xeon®Processors), one or more output devices 604 (e.g., LCD), one or morenetwork interfaces 606, one or more input devices 608 (e.g., mouse,keyboard, touch-sensitive display) and one or more non-transitorycomputer-readable mediums 612 b and memory 612 a (e.g., RAM, ROM, SDRAM,hard disk, optical disk, flash memory, etc.). These components canexchange communications and data over one or more communication channels610 (e.g., buses), which can utilize various hardware and software forfacilitating the transfer of data and control signals betweencomponents.

The term “non-transitory computer-readable medium” refers to any mediumthat participates in providing instructions to processor 602 forexecution, including without limitation, non-volatile media (e.g.,optical or magnetic disks), volatile media (e.g., memory) andtransmission media. Transmission media includes, without limitation,coaxial cables, copper wire and fiber optics.

Computer-readable mediums 612 b or memory 612 a can further includeoperating system 614 (e.g., Mac OS® server, Windows® NT server), networkcommunication module 616 and dynamic content presentation module 618.Operating system 614 can be multi-user, multiprocessing, multitasking,multithreading, real time, etc. Operating system 614 performs basictasks, including but not limited to: recognizing input from andproviding output to devices 604; keeping track and managing files anddirectories on storage devices 612 b and memory 612 a; controllingperipheral devices; and managing traffic on the one or morecommunication channels 610. Network communications module 616 includesvarious components for establishing and maintaining network connections(e.g., software for implementing communication protocols, such asTCP/IP, HTTP, etc.). Dynamic content presentation module 618 providesthe features and performs the process, described in reference to FIGS.1-17 .

Architecture 600 can be included in any computer device, including oneor more server computers each having one or more processing cores.Architecture 600 can be implemented in a parallel processing orpeer-to-peer infrastructure or on a single device with one or moreprocessors. Software can include multiple software components or can bea single body of code.

The described features can be implemented advantageously in one or morecomputer programs that are executable on a programmable system includingat least one programmable processor coupled to receive data andinstructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language (e.g., SWIFT, Objective-C, C#, Java),including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, component,subroutine, a browser-based web application, or other unit suitable foruse in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors orcores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random-access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to communicate with, one or more mass storage devices forstoring data files; such devices include magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andoptical disks. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices, such as EPROM,EEPROM, and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor or a retina display device fordisplaying information to the user. The computer can have a touchsurface input device (e.g., a touch screen) or a keyboard and a pointingdevice such as a mouse or a trackball by which the user can provideinput to the computer. The computer can have a voice input device forreceiving voice commands from the user.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a LAN, a WAN, and thecomputers and networks forming the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

A system of one or more computers can be configured to performparticular actions by virtue of having software, firmware, hardware, ora combination of them installed on the system that in operation causesor cause the system to perform the actions. One or more computerprograms can be configured to perform particular actions by virtue ofincluding instructions that, when executed by data processing apparatus,cause the apparatus to perform the actions.

One or more features or steps of the disclosed embodiments may beimplemented using an Application Programming Interface (API). An API maydefine on or more parameters that are passed between a callingapplication and other software code (e.g., an operating system, libraryroutine, function) that provides a service, that provides data, or thatperforms an operation or a computation. The API may be implemented asone or more calls in program code that send or receive one or moreparameters through a parameter list or other structure based on a callconvention defined in an API specification document. A parameter may bea constant, a key, a data structure, an object, an object class, avariable, a data type, a pointer, an array, a list, or another call. APIcalls and parameters may be implemented in any programming language. Theprogramming language may define the vocabulary and calling conventionthat a programmer will employ to access functions supporting the API. Insome implementations, an API call may report to an application thecapabilities of a device running the application, such as inputcapability, output capability, processing capability, power capability,communications capability, etc.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable sub combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to a subcombination or variation of a sub combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors, input data describing one or more facilities of a microgridor distributed energy resources (DER) system; generating, by the one ormore processors, indices for the input data; training, by the one ormore processors, a machine model using the indices; applying, by the oneor more processors, the machine learning model to a set of inputplanning parameters; and reporting, by the one or more processors, aplanning solution for the set of input planning parameters.
 2. Themethod of claim 1, wherein the machine learning model is a multilayerneural network.
 3. The method of claim 1, wherein the indices includeenergy intensity for a building type as an indicator of an hourly load.4. The method of claim 1, wherein the indices include geographiclocation as an indicator for solar radiation or hourly average outsidetemperature for a utility service area.
 5. The method of claim 1,wherein the indices include customer type as an indicator for an energyprovider or utility costs.
 6. The method of claim 1, wherein the indicesinclude geographic location as an indicator for changed technologycosts.
 7. The method of claim 1, wherein the set of input planningparameters includes at least one of hourly load profiles for each enduse, energy provider data, natural gas costs, solar radiation data,hourly average outside temperatures or technology options.
 8. A systemcomprising: one or more processors; memory storing instructions thatwhen executed by the one or more processors, cause the one or moreprocessors to perform operations comprising: receiving input datadescribing one or more facilities of a microgrid or distributed energyresources (DER) system; generating indices for the input data; traininga machine learning model using the indices; and applying the machinelearning model to a set of input planning parameters; and reporting aplanning solution for the set of input planning parameters.
 9. Thesystem of claim 8, wherein the machine learning model is a multilayerneural network.
 10. The system of claim 8, wherein the indices includeenergy intensity for a building type as an indicator of an hourly load.11. The system of claim 8, wherein the indices include geographiclocation as an indicator for solar radiation or hourly average outsidetemperature for a utility service area.
 12. The system of claim 8,wherein the indices include customer type as an indicator for an energyprovider or utility costs.
 13. The system of claim 8, wherein theindices include geographic location as an indicator for changedtechnology costs.
 14. The system of claim 8, wherein the set of inputplanning parameters includes at least one of hourly load profiles foreach end use, energy provider data, natural gas costs, solar radiationdata, hourly average outside temperatures or technology options.
 15. Anon-transitory, computer-readable storage medium having instructionsstored thereon, that when executed by one or more processors, cause theone or more processors to perform operations comprising: receiving inputdata describing one or more facilities of a microgrid or distributedenergy resources (DER) system; generating indices for the input data;training a machine learning model using the indices; applying themachine learning model to a set of input planning parameters; andreporting a planning solution for the set of input planning parameters.16. The non-transitory, computer-readable storage medium of claim 15,wherein the machine learning model is a multilayer neural network. 17.The non-transitory, computer-readable storage medium of claim 15,wherein the indices include energy intensity for a building type as anindicator of an hourly load.
 18. The non-transitory, computer-readablestorage medium of claim 15, wherein the indices include geographiclocation as an indicator for solar radiation or hourly average outsidetemperature for a utility service area.
 19. The non-transitory,computer-readable storage medium of claim 15, wherein the indicesinclude customer type as an indicator for an energy provider or utilitycosts.
 20. The non-transitory, computer-readable storage medium of claim15, wherein the indices include geographic location as an indicator forchanged technology costs.